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When Straight Lines Aren't Enough: Intro to Polynomial Regression
access_time 2025-04-26T09:44:07.483Z face Nerchuko
Beyond Straight Lines: Understanding Polynomial Regression Learn how to model curvy relationships in your data with this powerful technique. When Straight Lines Aren't Enough: Intro to Polynomial Regression We've seen how Simple and Multiple Linear Regression try to fit a straight line (or a flat pl...
Moving Beyond Simple: Multiple Linear Regression
access_time 2025-04-26T08:52:05.831Z face Nerchuko
Multiple Linear Regression: Predicting with More Power Go beyond single factors and learn how multiple inputs influence an outcome. Moving Beyond Simple: Multiple Linear Regression In Simple Linear Regression (SLR), we saw how to predict an outcome (like house price) using just one input factor (lik...
What is Simple Linear Regression?
access_time 2025-04-26T08:43:30.559Z face Nerchuko
Simple Linear Regression Explained Clearly Understanding the basics of predicting values with straight lines. What is Simple Linear Regression? Imagine you want to predict something – like a house price, a student's score, or maybe sales figures. Often, you suspect that *another* factor influences i...
Data Preprocessing: Preparing Your Data for Machine Learning
access_time 2025-04-26T08:36:14.895Z face Nerchuko
Data Preprocessing (A-Z) – Machine Learning Made Easy Essential techniques to prepare your data for Machine Learning models. Data Preprocessing: Preparing Your Data for Machine Learning For Machine Learning models to work well, the data we feed them needs to be clean and in the right format. Just li...
Bias, Variance, Overfitting, and Underfitting Explained
access_time 2025-04-26T08:08:41.731Z face Nerchuko
Bias, Variance, Overfitting, and Underfitting Explained Mastering the trade-offs for better machine learning models Overview Key Learning Objectives Understand the concepts of bias and variance in machine learning Identify and differentiate between underfitting and overfitting Learn how to diagnose ...